Chapter 4 MAG catalogue
4.1 Gut microbiota
4.1.1 Genome phylogeny
# Generate the phylum color heatmap
phylum_heatmap <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
arrange(match(genome, genome_tree$tip.label)) %>%
select(genome,phylum) %>%
mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
column_to_rownames(var = "genome")
# Generate new species table
newspecies_table <- genome_metadata %>%
mutate(newspecies=ifelse(species=="s__","Y","N")) %>%
select(genome,newspecies) %>%
column_to_rownames(var = "genome")
# Generate basal tree
circular_tree <- force.ultrametric(genome_tree, method="extend") %>% # extend to ultrametric for the sake of visualisation
ggtree(., layout="fan", open.angle=10, size=0.5)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************# Add phylum ring
# circular_tree <- gheatmap(circular_tree, phylum_heatmap, offset=0.55, width=0.1, colnames=FALSE) +
# scale_fill_manual(values=phylum_colors) +
# geom_tiplab2(size=1, hjust=-0.1) +
# theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0))
circular_tree <- gheatmap(circular_tree, phylum_heatmap, offset=0.05, width=0.05, colnames=FALSE) +
scale_fill_manual(values=phylum_colors) +
labs(fill="Phylum")
#theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0))
# Flush color scale to enable a new color scheme in the next ring
circular_tree <- circular_tree + new_scale_fill()
# Add completeness ring
circular_tree <- circular_tree +
new_scale_fill() +
scale_fill_gradient(low = "#d1f4ba", high = "#f4baba") +
geom_fruit(
data=genome_metadata,
geom=geom_bar,
mapping = aes(x=completeness, y=genome, fill=contamination),
offset = 0.24,
pwidth = 0.1,
orientation="y",
stat="identity")+
labs(fill="Contamination")
# Add genome-size ring
circular_tree <- circular_tree + new_scale_fill()
circular_tree <- gheatmap(circular_tree, newspecies_table, offset=0.3, width=0.05, colnames=FALSE) +
scale_fill_manual(values=c("#f4f4f4","#74C8AE"))+
labs(fill="New species")
#theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0))
circular_tree <- circular_tree +
geom_fruit(
data=genome_metadata,
geom=geom_bar,
mapping = aes(x=length, y=genome),
fill = "#1e6e55",
offset = 0.05,
orientation="y",
stat="identity")
# Add text
circular_tree <- circular_tree +
annotate('text', x=3.1, y=0, label=' Phylum', family='arial', size=3.5) +
annotate('text', x=3.7, y=0, label=' Genome quality', family='arial', size=3.5) +
annotate('text', x=4.1, y=0, label=' Genome size', family='arial', size=3.5) +
annotate('text', x=3.4, y=0, label=' New species', family='arial', size=3.5)
#Plot circular tree
circular_tree %>% open_tree(30) %>% rotate_tree(90)
4.1.2 Taxonomy overview
tax_mag <-genome_metadata %>%
group_by(phylum) %>%
summarise(mag_n=n())
tax_mag %>%
mutate(percetage_mag=round(mag_n*100/sum(mag_n), 2)) %>%
arrange(-percetage_mag) %>%
tt()| phylum | mag_n | percetage_mag |
|---|---|---|
| p__Bacteroidota | 191 | 35.44 |
| p__Bacillota_A | 175 | 32.47 |
| p__Pseudomonadota | 87 | 16.14 |
| p__Bacillota | 23 | 4.27 |
| p__Verrucomicrobiota | 18 | 3.34 |
| p__Cyanobacteriota | 12 | 2.23 |
| p__Bacillota_C | 11 | 2.04 |
| p__Desulfobacterota | 7 | 1.30 |
| p__Fusobacteriota | 6 | 1.11 |
| p__Bacillota_B | 4 | 0.74 |
| p__Deferribacterota | 2 | 0.37 |
| p__Elusimicrobiota | 2 | 0.37 |
| p__Chlamydiota | 1 | 0.19 |
4.1.3 Mag size (MB)
genome_metadata <- genome_metadata %>%
mutate(corrected_size=100*length/completeness) %>%
arrange(completeness)# A tibble: 1 × 1
Average_corrected_size
<dbl>
1 3245297.4.1.4 Genome quality
genome_metadata %>%
summarise(completeness_mean=mean(completeness) %>% round(2) %>% as.character(),
completeness_sd=sd(completeness) %>% round(2) %>% as.character(),
contamination_mean=mean(contamination) %>% round(2),
contamination_sd=sd(contamination) %>% round(2)) %>%
unite("Completeness",completeness_mean, completeness_sd, sep = " ± ", remove = TRUE) %>%
unite("Contamination",contamination_mean, contamination_sd, sep = " ± ", remove = TRUE) %>%
tt()| Completeness | Contamination |
|---|---|
| 85.01 ± 16.2 | 1.68 ± 2.25 |
#Generate quality biplot
genome_biplot <- genome_metadata %>%
select(c(genome,domain,phylum,completeness,contamination,length)) %>%
arrange(match(genome, rev(genome_tree$tip.label))) %>% #sort MAGs according to phylogenetic tree
ggplot(aes(x=completeness,y=contamination,size=length,color=phylum)) +
geom_point(alpha=0.7) +
ylim(c(10,0)) +
scale_color_manual(values=phylum_colors) +
labs(y= "Contamination", x = "Completeness") +
theme_classic() +
theme(legend.position = "none")
#Generate contamination boxplot
genome_contamination <- genome_metadata %>%
ggplot(aes(y=contamination)) +
ylim(c(10,0)) +
geom_boxplot(colour = "#999999", fill="#cccccc") +
theme_void() +
theme(legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
plot.margin = unit(c(0, 0, 0.40, 0),"inches")) #add bottom-margin (top, right, bottom, left)
#Generate completeness boxplot
genome_completeness <- genome_metadata %>%
ggplot(aes(x=completeness)) +
xlim(c(50,100)) +
geom_boxplot(colour = "#999999", fill="#cccccc") +
theme_void() +
theme(legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
plot.margin = unit(c(0, 0, 0, 0.50),"inches")) #add left-margin (top, right, bottom, left)
#Render composite figure
grid.arrange(grobs = list(genome_completeness,genome_biplot,genome_contamination),
layout_matrix = rbind(c(1,1,1,1,1,1,1,1,1,1,1,4),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3)))
4.1.5 Functional overview
4.1.5.1 Predicted genes
genome_annotations <- read_tsv("data/gut/genome_annotations.tsv.xz") %>%
rename(gene=1, genome=2, contig=3)
# Predicted genes
pred_genes <- genome_annotations %>%
nrow()
cat(pred_genes)12196674.1.5.2 Number of annotated genes and percentages
# Some annotation
genome_annota <- genome_annotations %>%
filter(if_any(c(kegg_id, pfam_hits, cazy_hits), ~ !is.na(.))) %>%
nrow()
cat(genome_annota)942120[1] 77.244034.1.5.3 Number of KEGG annotatated genes and percentages
# KEGG annotation
kegg_annota <- genome_annotations %>%
filter(!is.na(kegg_id)) %>%
nrow()
cat(kegg_annota)557611[1] 59.18683# Aggregate basal GIFT into elements
function_table <- genome_gifts %>%
to.elements(., GIFT_db)
# Generate basal tree
function_tree <- force.ultrametric(genome_tree, method="extend") %>%
ggtree(., size = 0.3)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************#Add phylum colors next to the tree tips
function_tree <- gheatmap(function_tree, phylum_heatmap, offset=0, width=0.1, colnames=FALSE) +
scale_fill_manual(values=phylum_colors) +
labs(fill="Phylum")
#Reset fill scale to use a different colour profile in the heatmap
function_tree <- function_tree + new_scale_fill()
#Add functions heatmap
function_tree <- gheatmap(function_tree, function_table, offset=0.5, width=3.5, colnames=FALSE) +
vexpand(.08) +
coord_cartesian(clip = "off") +
scale_fill_gradient(low = "#f4f4f4", high = "steelblue", na.value="white") +
labs(fill="GIFT")
#Reset fill scale to use a different colour profile in the heatmap
function_tree <- function_tree + new_scale_fill()
# Add completeness barplots
function_tree <- function_tree +
geom_fruit(data=genome_metadata,
geom=geom_bar,
grid.params=list(axis="x", text.size=2, nbreak = 1),
axis.params=list(vline=TRUE),
mapping = aes(x=length, y=genome, fill=completeness),
offset = 3.8,
orientation="y",
stat="identity") +
scale_fill_gradient(low = "#cf8888", high = "#a2cc87") +
labs(fill="Genome\ncompleteness")
function_tree
4.1.6 Functional ordination
gift_pcoa <- genome_gifts %>%
to.elements(., GIFT_db) %>%
as.data.frame() %>%
vegdist(method="euclidean") %>%
pcoa()
gift_pcoa_rel_eigen <- gift_pcoa$values$Relative_eig[1:10]
# Get genome positions
gift_pcoa_vectors <- gift_pcoa$vectors %>% #extract vectors
as.data.frame() %>%
select(Axis.1,Axis.2) # keep the first 2 axes
gift_pcoa_eigenvalues <- gift_pcoa$values$Eigenvalues[c(1,2)]
gift_pcoa_gifts <- cov(genome_gifts, scale(gift_pcoa_vectors)) %*% diag((gift_pcoa_eigenvalues/(nrow(genome_gifts)-1))^(-0.5)) %>%
as.data.frame() %>%
rename(Axis.1=1,Axis.2=2) %>%
rownames_to_column(var="label") %>%
#get function summary vectors
mutate(func=substr(label,1,3)) %>%
group_by(func) %>%
summarise(Axis.1=mean(Axis.1),
Axis.2=mean(Axis.2)) %>%
rename(label=func) %>%
filter(!label %in% c("S01","S02","S03"))set.seed(101)
scale <- 20 # scale for vector loadings
gift_pcoa_vectors %>%
rownames_to_column(var="genome") %>%
left_join(genome_metadata, by="genome") %>%
ggplot() +
#genome positions
scale_color_manual(values=phylum_colors)+
geom_point(aes(x=Axis.1,y=Axis.2, color=phylum, size=length),
alpha=0.9, shape=16) +
#scale_color_manual(values=phylum_colors) +
scale_size_continuous(range = c(0.1,5)) +
#loading positions
geom_segment(data=gift_pcoa_gifts,
aes(x=0, y=0, xend=Axis.1 * scale, yend=Axis.2 * scale),
arrow = arrow(length = unit(0.3, "cm"),
type = "open",
angle = 25),
linewidth = 0.5,
color = "black") +
#Primary and secondary scale adjustments
scale_x_continuous(name = paste0("PCoA1 (",round(gift_pcoa_rel_eigen[1]*100, digits = 2), " %)"),
sec.axis = sec_axis(~ . / scale, name = "Loadings on PCoA1")
) +
scale_y_continuous(name = paste0("PCoA2 (",round(gift_pcoa_rel_eigen[2]*100, digits = 2), " %)"),
sec.axis = sec_axis(~ . / scale, name = "Loadings on PCoA2")
) +
geom_label_repel(data = gift_pcoa_gifts,
aes(label = label, x = Axis.1 * scale, y = Axis.2 * scale),
segment.color = 'transparent') +
xlim(-4.5,4.5) +
ylim(-3,2.5) +
theme_minimal() +
theme(legend.position = "none")
4.2 Skin microbiota
4.2.1 Genome phylogeny
# Generate the phylum color heatmap
phylum_heatmap <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
arrange(match(genome, genome_tree$tip.label)) %>%
select(genome,phylum) %>%
mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
column_to_rownames(var = "genome")
# Generate new species table
newspecies_table <- genome_metadata %>%
mutate(newspecies=ifelse(species=="s__","Y","N")) %>%
select(genome,newspecies) %>%
column_to_rownames(var = "genome")
# Generate basal tree
circular_tree <- force.ultrametric(genome_tree, method="extend") %>% # extend to ultrametric for the sake of visualisation
ggtree(., layout="fan", open.angle=10, size=0.5)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************# Add phylum ring
# circular_tree <- gheatmap(circular_tree, phylum_heatmap, offset=0.55, width=0.1, colnames=FALSE) +
# scale_fill_manual(values=phylum_colors) +
# geom_tiplab2(size=1, hjust=-0.1) +
# theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0))
circular_tree <- gheatmap(circular_tree, phylum_heatmap, offset=0.05, width=0.05, colnames=FALSE) +
scale_fill_manual(values=phylum_colors) +
labs(fill="Phylum")
#theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0))
# Flush color scale to enable a new color scheme in the next ring
circular_tree <- circular_tree + new_scale_fill()
# Add completeness ring
circular_tree <- circular_tree +
new_scale_fill() +
scale_fill_gradient(low = "#d1f4ba", high = "#f4baba") +
geom_fruit(
data=genome_metadata,
geom=geom_bar,
mapping = aes(x=completeness, y=genome, fill=contamination),
offset = 0.24,
pwidth = 0.1,
orientation="y",
stat="identity")+
labs(fill="Contamination")
# Add genome-size ring
circular_tree <- circular_tree + new_scale_fill()
circular_tree <- gheatmap(circular_tree, newspecies_table, offset=0.3, width=0.05, colnames=FALSE) +
scale_fill_manual(values=c("#f4f4f4","#74C8AE"))+
labs(fill="New species")
#theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0))
circular_tree <- circular_tree +
geom_fruit(
data=genome_metadata,
geom=geom_bar,
mapping = aes(x=length, y=genome),
fill = "#1e6e55",
offset = 0.05,
orientation="y",
stat="identity")
# Add text
circular_tree <- circular_tree +
annotate('text', x=2.2, y=0, label=' Phylum', family='arial', size=3.5) +
annotate('text', x=2.7, y=0, label=' Genome quality', family='arial', size=3.5) +
annotate('text', x=3.0, y=0, label=' Genome size', family='arial', size=3.5) +
annotate('text', x=2.5, y=0, label=' New species', family='arial', size=3.5)
#Plot circular tree
circular_tree %>% open_tree(30) %>% rotate_tree(90)
4.2.3 Genome quality
genome_metadata %>%
summarise(completeness_mean=mean(completeness) %>% round(2) %>% as.character(),
completeness_sd=sd(completeness) %>% round(2) %>% as.character(),
contamination_mean=mean(contamination) %>% round(2),
contamination_sd=sd(contamination) %>% round(2)) %>%
unite("Completeness",completeness_mean, completeness_sd, sep = " ± ", remove = TRUE) %>%
unite("Contamination",contamination_mean, contamination_sd, sep = " ± ", remove = TRUE) %>%
tt()| Completeness | Contamination |
|---|---|
| 77.24 ± 17.04 | 4.48 ± 2.42 |
#Generate quality biplot
genome_biplot <- genome_metadata %>%
select(c(genome,domain,phylum,completeness,contamination,length)) %>%
arrange(match(genome, rev(genome_tree$tip.label))) %>% #sort MAGs according to phylogenetic tree
ggplot(aes(x=completeness,y=contamination,size=length,color=phylum)) +
geom_point(alpha=0.7) +
ylim(c(10,0)) +
scale_color_manual(values=phylum_colors) +
labs(y= "Contamination", x = "Completeness") +
theme_classic() +
theme(legend.position = "none")
#Generate contamination boxplot
genome_contamination <- genome_metadata %>%
ggplot(aes(y=contamination)) +
ylim(c(10,0)) +
geom_boxplot(colour = "#999999", fill="#cccccc") +
theme_void() +
theme(legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
plot.margin = unit(c(0, 0, 0.40, 0),"inches")) #add bottom-margin (top, right, bottom, left)
#Generate completeness boxplot
genome_completeness <- genome_metadata %>%
ggplot(aes(x=completeness)) +
xlim(c(50,100)) +
geom_boxplot(colour = "#999999", fill="#cccccc") +
theme_void() +
theme(legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
plot.margin = unit(c(0, 0, 0, 0.50),"inches")) #add left-margin (top, right, bottom, left)
#Render composite figure
grid.arrange(grobs = list(genome_completeness,genome_biplot,genome_contamination),
layout_matrix = rbind(c(1,1,1,1,1,1,1,1,1,1,1,4),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3)))
4.2.4 Functional overview
4.2.4.1 Predicted genes
genome_annotations <- read_tsv("data/skin/genome_annotations.tsv.xz") %>%
rename(gene=1, genome=2, contig=3)
# Predicted genes
pred_genes <- genome_annotations %>%
nrow()
cat(pred_genes)1143034.2.4.2 Number of annotated genes and percentages
# Some annotation
genome_annota <- genome_annotations %>%
filter(if_any(c(kegg_id, pfam_hits, cazy_hits), ~ !is.na(.))) %>%
nrow()
cat(genome_annota)88006[1] 76.99364.2.4.3 Number of KEGG annotatated genes and percentages
# KEGG annotation
kegg_annota <- genome_annotations %>%
filter(!is.na(kegg_id)) %>%
nrow()
cat(kegg_annota)52991[1] 60.21294# Aggregate basal GIFT into elements
function_table <- genome_gifts %>%
to.elements(., GIFT_db)
# Generate basal tree
function_tree <- force.ultrametric(genome_tree, method="extend") %>%
ggtree(., size = 0.3)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************#Add phylum colors next to the tree tips
function_tree <- gheatmap(function_tree, phylum_heatmap, offset=0, width=0.1, colnames=FALSE) +
scale_fill_manual(values=phylum_colors) +
labs(fill="Phylum")
#Reset fill scale to use a different colour profile in the heatmap
function_tree <- function_tree + new_scale_fill()
#Add functions heatmap
function_tree <- gheatmap(function_tree, function_table, offset=0.5, width=3.5, colnames=FALSE) +
vexpand(.08) +
coord_cartesian(clip = "off") +
scale_fill_gradient(low = "#f4f4f4", high = "steelblue", na.value="white") +
labs(fill="GIFT")
#Reset fill scale to use a different colour profile in the heatmap
function_tree <- function_tree + new_scale_fill()
# Add completeness barplots
function_tree <- function_tree +
geom_fruit(data=genome_metadata,
geom=geom_bar,
grid.params=list(axis="x", text.size=2, nbreak = 1),
axis.params=list(vline=TRUE),
mapping = aes(x=length, y=genome, fill=completeness),
offset = 3.8,
orientation="y",
stat="identity") +
scale_fill_gradient(low = "#cf8888", high = "#a2cc87") +
labs(fill="Genome\ncompleteness")
function_tree
4.2.5 Functional ordination
gift_pcoa <- genome_gifts %>%
to.elements(., GIFT_db) %>%
as.data.frame() %>%
vegdist(method="euclidean") %>%
pcoa()
gift_pcoa_rel_eigen <- gift_pcoa$values$Relative_eig[1:10]
# Get genome positions
gift_pcoa_vectors <- gift_pcoa$vectors %>% #extract vectors
as.data.frame() %>%
select(Axis.1,Axis.2) # keep the first 2 axes
gift_pcoa_eigenvalues <- gift_pcoa$values$Eigenvalues[c(1,2)]
gift_pcoa_gifts <- cov(genome_gifts, scale(gift_pcoa_vectors)) %*% diag((gift_pcoa_eigenvalues/(nrow(genome_gifts)-1))^(-0.5)) %>%
as.data.frame() %>%
rename(Axis.1=1,Axis.2=2) %>%
rownames_to_column(var="label") %>%
#get function summary vectors
mutate(func=substr(label,1,3)) %>%
group_by(func) %>%
summarise(Axis.1=mean(Axis.1),
Axis.2=mean(Axis.2)) %>%
rename(label=func) %>%
filter(!label %in% c("S01","S02","S03"))set.seed(101)
scale <- 20 # scale for vector loadings
gift_pcoa_vectors %>%
rownames_to_column(var="genome") %>%
left_join(genome_metadata, by="genome") %>%
ggplot() +
#genome positions
scale_color_manual(values=phylum_colors)+
geom_point(aes(x=Axis.1,y=Axis.2, color=phylum, size=length),
alpha=0.9, shape=16) +
#scale_color_manual(values=phylum_colors) +
scale_size_continuous(range = c(0.1,5)) +
#loading positions
geom_segment(data=gift_pcoa_gifts,
aes(x=0, y=0, xend=Axis.1 * scale, yend=Axis.2 * scale),
arrow = arrow(length = unit(0.3, "cm"),
type = "open",
angle = 25),
linewidth = 0.5,
color = "black") +
#Primary and secondary scale adjustments
scale_x_continuous(name = paste0("PCoA1 (",round(gift_pcoa_rel_eigen[1]*100, digits = 2), " %)"),
sec.axis = sec_axis(~ . / scale, name = "Loadings on PCoA1")
) +
scale_y_continuous(name = paste0("PCoA2 (",round(gift_pcoa_rel_eigen[2]*100, digits = 2), " %)"),
sec.axis = sec_axis(~ . / scale, name = "Loadings on PCoA2")
) +
geom_label_repel(data = gift_pcoa_gifts,
aes(label = label, x = Axis.1 * scale, y = Axis.2 * scale),
segment.color = 'transparent') +
xlim(-4.5,4.5) +
ylim(-3,2.5) +
theme_minimal() +
theme(legend.position = "none")
# Generate the tSNE ordination
set.seed(1001)
tSNE_function <- Rtsne(X=function_table, dims = 2, perplexity = 13, check_duplicates = FALSE)
# Plot the ordination
function_ordination <- tSNE_function$Y %>%
as.data.frame() %>%
mutate(genome=rownames(function_table)) %>%
inner_join(genome_metadata, by="genome") %>%
rename(tSNE1="V1", tSNE2="V2") %>%
select(genome,phylum,tSNE1,tSNE2, length) %>%
ggplot(aes(x = tSNE1, y = tSNE2, color = phylum, size=length))+
geom_point(shape=16, alpha=0.7) +
scale_color_manual(values=phylum_colors) +
ylim(-30,40)+
xlim(-40,40)+
theme_minimal() +
labs(color="Phylum", size="Genome size") +
guides(color = guide_legend(override.aes = list(size = 5))) # enlarge Phylum dots in legend
function_ordinationWarning: Removed 34 rows containing missing values or values outside the scale range (`geom_point()`).